A straightforward way is to incorporate the learned depth estimates into a conventional visual odometry algorithm to recover the absolute scale metric of poses [1]}. Learning depth estimation is a well-researched area in the computer vision community. For example, [2]}, [3]}, [4]}, [5]} provide per-pixel depths in a global scale by employing a trained deep neural model. Thus the so-called scale problem of conventional VO is mitigated.
Barnes et al. [6]} utilize both the predicted depth maps and ephemeral masks (i.e. the area of moving objects) into a VO system to improve its robustness to moving objects.
Zhan et al. [7]} integrate the learned depth and optical flow predictions into a conventional visual odometry model, achieving competitive performance over other baselines.
Other works combine physical motion models with deep neural network e.g. via a differentiable Kalman filter [8]}, and a particle filter [9]}. The physical model serves as an algorithmic prior in the learning process.
Furthermore, D3VO [10]} incorporates the deep predictions of depth, pose, and uncertainty into a direct visual odometry.
